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研究生:林博堯
研究生(外文):Bo-Yao Lin
論文名稱:支援 LTE-A 物聯通訊之群組形成通訊協定設計
論文名稱(外文):Design of Cluster Formation Protocols for Machine-to-Machine Applications in LTE-Advanced Networks
指導教授:謝宏昀
口試委員:趙禧綠葉書蘋
口試日期:2013-07-29
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:英文
論文頁數:93
中文關鍵詞:物聯網群組通訊協定LTE-A
外文關鍵詞:M2MClusterProtocolLTE-A
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在物聯通訊中,大量的裝置被佈建在一區域且形成一個擁擠的網路。由於無線網路的頻譜資源相當有限,如何有效地利用這些有限的資源被視為是支援物聯網的一項關鍵技術。物聯裝置之群組 (cluster) 形成可以有效地減緩為了支援物聯通訊時造成的無線網路資源匱乏的情況,此方法利用了空間因素來增加資料傳輸時的效率。在過去相關的文獻中,大部分是在探討如何形成群組之演算法,只有鮮少部分有涉及到通訊協定的層面。本篇論文的特色在於提出使物聯裝置能形成群組之通訊協定,並且能與實際的無線系統 3GPP LTE-A 做結合。此項研究的困難點在於如何透過實際的無線系統去獲得必要的資訊來建立群組,以及使物聯裝置能自動的形成群組而沒有人們的干涉。為解決這個問題,我們將整個通訊協定的程序分成兩大部分,第一部分主要是每個裝置收集一些必要的資訊,包含通道品質與物聯裝置的位置資訊,來為之後建立群組做準備。第二部分則是藉由第一部分所獲得的資訊,根據不同的目標來建立群組。由於物聯通訊有各種應用,我們首先針對資料較不具相關性的應用設計,以最大化可支援通訊之機器數量為目標。以此方向設計,我們可以使物聯裝置在短時間內做有效地傳輸。之後再考慮有相關性的情境,並以最小化群組代表 (cluster head)傳輸的能量消耗為目標。因為在群組結構中,群組代表在傳輸時所消耗的能量較大。如果我們能藉由資料的相關性有效地降低群組代表在傳輸時所消耗的能量,將可延長整體網路的壽命。在提出的不具資料相關性的通訊協定中,我們可以從模擬結果觀察到對大部分的物聯裝置而言,本論文提出的群組形成方法可以讓他們得到良好的訊雜比 (SINR),且形成群組所花的時間與代價都不高。此外,在我們所提出的通訊協定下,我們允許相隔較遠的群組能重複使用相同的頻段,使得對頻譜資源能更有效地使用。 在考慮相關性的情境中,我們可以從模擬結果中觀察到它具有較低的能量消耗,其在傳輸時的平均能量消耗比資料不具相關性所設計的通訊協定減少大約5%。

In machine-to-machine (M2M) communications, large amount of machine type communication (MTC) devices in a cell may result in the scarcity of time frequency resource in wireless networks. Grouping MTC devices into cluster structure utilizes the spatial structure to distribute the access attempts and data transmissions. Most related work on cluster formation focuses on the design of algorithms without considering how to combine the cluster methods with an actual wireless system. The main feature of the proposed protocols is that they can be embedded into the realistic LTE-A wireless system. We decouple the overall protocol into two parts. One is every MTC device collect the necessary information including channel quality and positions of other MTC devices. This information would be used in the second part for constructing cluster structure. The second part is to construct the cluster structure by means of different target of protocols through the information collected in first part. Due to the various applications in M2M communications, we first focus on the data without correlation property and the objective is maximizing the number of supported MTC device. Based on this direction, we can make all MTC devices transmit data in a short period of time effectively. Afterwards, we consider the data with correlated scenario, and the goal is to minimize the energy consumption of all cluster heads. In cluster structure, cluster heads have more energy consumption than cluster members. If we can effectively reduce the energy consumption by data correlation, we would prolong the network life time. In the protocol without data correlation among MTC devices, most MTC devices have good Signal-to-Interference-plus-Noise Ratio (SINR) in the proposed clustering method and the time and cost for constructing cluster is quite low. In the protocol with correlated data aggregation, it can reduce 5 \% energy consumption than the protocol which focus on the data without correlation.

TABLE OF CONTENTS
ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
CHAPTER 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . 1
CHAPTER 2 BACKGROUND AND RELATED WORK . . . . . 4
2.1 Architecture of Machine-to-machine Communications . . . . . . . 4
2.2 Access Procedures in LTE-A . . . . . . . . . . . . . . . . . . . . . 5
2.2.1 Radio Access Network Overload and Extended Access Barring
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
2.3 Cluster Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.3.1 Cluster in Data Analysis . . . . . . . . . . . . . . . . . . . 9
2.3.2 Clustering Nodes in Wireless Network . . . . . . . . . . . . 11
2.4 Summary and Motivation . . . . . . . . . . . . . . . . . . . . . . . 15
CHAPTER 3 SYSTEM MODEL AND CLUSTER FORMATION
ALGORITHM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.2 Distributed Clustering Algorithm . . . . . . . . . . . . . . . . . . 18
3.3 Centralized Clustering Algorithm . . . . . . . . . . . . . . . . . . . 26
3.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
CHAPTER 4 DISTRIBUTED CLUSTER FORMATION PRO-
TOCOL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.1 Timeline of Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 Time-Frequency Structure and Physical Channels in LTE-A . . . . 35
4.3 Distributed Cluster Formation Protocol . . . . . . . . . . . . . . . 37
4.3.1 Parameter Estimation Phase . . . . . . . . . . . . . . . . . 38
4.3.2 Clustering Phase . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4 Collision Handler for Cluster Head Message . . . . . . . . . . . . . 46
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
CHAPTER 5 ANALYSIS OF CLUSTER FORMATION PROTO-
COL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.1 Point Process Theory . . . . . . . . . . . . . . . . . . . . . . . . . 50
5.2 System Topology and Assumptions . . . . . . . . . . . . . . . . . . 51
5.3 Success Probability in Parameter Estimation Phase . . . . . . . . 53
5.4 Clustering Threshold in Clustering Phase . . . . . . . . . . . . . . 57
5.5 Setting of Counter Value . . . . . . . . . . . . . . . . . . . . . . . 62
5.6 The Overhead of The Distributed Clustering Protocol . . . . . . . 66
5.7 Simulation of Distributed Clustering Protocol . . . . . . . . . . . . 68
5.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
CHAPTER 6 CLUSTER FORMATION PROTOCOL FOR COR-
RELATED DATA AGGREGATION . . . . . . . . . . . . . . . . . 76
6.1 Correlation Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
6.2 Protocol with Correlated Data Aggregation . . . . . . . . . . . . . 77
6.3 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
CHAPTER 7 CONCLUSION AND FUTURE WORK . . . . . . 87
7.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

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